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Proxy Config.yaml

Set model list, api_base, api_key, temperature & proxy server settings (master-key) on the config.yaml.

Param NameDescription
model_listList of supported models on the server, with model-specific configs
router_settingslitellm Router settings, example routing_strategy="least-busy" see all
litellm_settingslitellm Module settings, example litellm.drop_params=True, litellm.set_verbose=True, litellm.api_base, litellm.cache see all
general_settingsServer settings, example setting master_key: sk-my_special_key
environment_variablesEnvironment Variables example, REDIS_HOST, REDIS_PORT

Complete List: Check the Swagger UI docs on <your-proxy-url>/#/config.yaml (e.g. http://0.0.0.0:8000/#/config.yaml), for everything you can pass in the config.yaml.

Quick Start

Set a model alias for your deployments.

In the config.yaml the model_name parameter is the user-facing name to use for your deployment.

In the config below:

  • model_name: the name to pass TO litellm from the external client
  • litellm_params.model: the model string passed to the litellm.completion() function

E.g.:

  • model=vllm-models will route to openai/facebook/opt-125m.
  • model=gpt-3.5-turbo will load balance between azure/gpt-turbo-small-eu and azure/gpt-turbo-small-ca
model_list:
- model_name: gpt-3.5-turbo ### RECEIVED MODEL NAME ###
litellm_params: # all params accepted by litellm.completion() - https://docs.litellm.ai/docs/completion/input
model: azure/gpt-turbo-small-eu ### MODEL NAME sent to `litellm.completion()` ###
api_base: https://my-endpoint-europe-berri-992.openai.azure.com/
api_key: "os.environ/AZURE_API_KEY_EU" # does os.getenv("AZURE_API_KEY_EU")
rpm: 6 # [OPTIONAL] Rate limit for this deployment: in requests per minute (rpm)
- model_name: bedrock-claude-v1
litellm_params:
model: bedrock/anthropic.claude-instant-v1
- model_name: gpt-3.5-turbo
litellm_params:
model: azure/gpt-turbo-small-ca
api_base: https://my-endpoint-canada-berri992.openai.azure.com/
api_key: "os.environ/AZURE_API_KEY_CA"
rpm: 6
- model_name: anthropic-claude
litellm_params:
model="bedrock/anthropic.claude-instant-v1"
### [OPTIONAL] SET AWS REGION ###
aws_region_name="us-east-1"
- model_name: vllm-models
litellm_params:
model: openai/facebook/opt-125m # the `openai/` prefix tells litellm it's openai compatible
api_base: http://0.0.0.0:8000
rpm: 1440
model_info:
version: 2

litellm_settings: # module level litellm settings - https://github.com/BerriAI/litellm/blob/main/litellm/__init__.py
drop_params: True
set_verbose: True

general_settings:
master_key: sk-1234 # [OPTIONAL] Only use this if you to require all calls to contain this key (Authorization: Bearer sk-1234)
info

For more provider-specific info, go here

Step 2: Start Proxy with config

$ litellm --config /path/to/config.yaml

Using Proxy - Curl Request, OpenAI Package, Langchain, Langchain JS

Calling a model group

Sends request to model where model_name=gpt-3.5-turbo on config.yaml.

If multiple with model_name=gpt-3.5-turbo does Load Balancing

curl --location 'http://0.0.0.0:8000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}
'

Save Model-specific params (API Base, API Keys, Temperature, Max Tokens, Seed, Organization, Headers etc.)

You can use the config to save model-specific information like api_base, api_key, temperature, max_tokens, etc.

All input params

Step 1: Create a config.yaml file

model_list:
- model_name: gpt-4-team1
litellm_params: # params for litellm.completion() - https://docs.litellm.ai/docs/completion/input#input---request-body
model: azure/chatgpt-v-2
api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
api_version: "2023-05-15"
azure_ad_token: eyJ0eXAiOiJ
seed: 12
max_tokens: 20
- model_name: gpt-4-team2
litellm_params:
model: azure/gpt-4
api_key: sk-123
api_base: https://openai-gpt-4-test-v-2.openai.azure.com/
temperature: 0.2
- model_name: openai-gpt-3.5
litellm_params:
model: openai/gpt-3.5-turbo
api_key: sk-123
organization: org-ikDc4ex8NB
temperature: 0.2
- model_name: mistral-7b
litellm_params:
model: ollama/mistral
api_base: your_ollama_api_base
headers: {
"HTTP-Referer": "litellm.ai",
"X-Title": "LiteLLM Server"
}

Step 2: Start server with config

$ litellm --config /path/to/config.yaml

Set Azure base_model for cost tracking

Problem: Azure returns gpt-4 in the response when azure/gpt-4-1106-preview is used. This leads to inaccurate cost tracking

Solution ✅ : Set base_model on your config so litellm uses the correct model for calculating azure cost

Example config with base_model

model_list:
- model_name: azure-gpt-3.5
litellm_params:
model: azure/chatgpt-v-2
api_base: os.environ/AZURE_API_BASE
api_key: os.environ/AZURE_API_KEY
api_version: "2023-07-01-preview"
model_info:
base_model: azure/gpt-4-1106-preview

You can view your cost once you set up Virtual keys or custom_callbacks

Load API Keys

Load API Keys from Environment

If you have secrets saved in your environment, and don't want to expose them in the config.yaml, here's how to load model-specific keys from the environment.

os.environ["AZURE_NORTH_AMERICA_API_KEY"] = "your-azure-api-key"
model_list:
- model_name: gpt-4-team1
litellm_params: # params for litellm.completion() - https://docs.litellm.ai/docs/completion/input#input---request-body
model: azure/chatgpt-v-2
api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
api_version: "2023-05-15"
api_key: os.environ/AZURE_NORTH_AMERICA_API_KEY

See Code

s/o to @David Manouchehri for helping with this.

Load API Keys from Azure Vault

  1. Install Proxy dependencies
$ pip install 'litellm[proxy]' 'litellm[extra_proxy]'
  1. Save Azure details in your environment
export["AZURE_CLIENT_ID"]="your-azure-app-client-id"
export["AZURE_CLIENT_SECRET"]="your-azure-app-client-secret"
export["AZURE_TENANT_ID"]="your-azure-tenant-id"
export["AZURE_KEY_VAULT_URI"]="your-azure-key-vault-uri"
  1. Add to proxy config.yaml
model_list: 
- model_name: "my-azure-models" # model alias
litellm_params:
model: "azure/<your-deployment-name>"
api_key: "os.environ/AZURE-API-KEY" # reads from key vault - get_secret("AZURE_API_KEY")
api_base: "os.environ/AZURE-API-BASE" # reads from key vault - get_secret("AZURE_API_BASE")

general_settings:
use_azure_key_vault: True

You can now test this by starting your proxy:

litellm --config /path/to/config.yaml

Set Custom Prompt Templates

LiteLLM by default checks if a model has a prompt template and applies it (e.g. if a huggingface model has a saved chat template in it's tokenizer_config.json). However, you can also set a custom prompt template on your proxy in the config.yaml:

Step 1: Save your prompt template in a config.yaml

# Model-specific parameters
model_list:
- model_name: mistral-7b # model alias
litellm_params: # actual params for litellm.completion()
model: "huggingface/mistralai/Mistral-7B-Instruct-v0.1"
api_base: "<your-api-base>"
api_key: "<your-api-key>" # [OPTIONAL] for hf inference endpoints
initial_prompt_value: "\n"
roles: {"system":{"pre_message":"<|im_start|>system\n", "post_message":"<|im_end|>"}, "assistant":{"pre_message":"<|im_start|>assistant\n","post_message":"<|im_end|>"}, "user":{"pre_message":"<|im_start|>user\n","post_message":"<|im_end|>"}}
final_prompt_value: "\n"
bos_token: "<s>"
eos_token: "</s>"
max_tokens: 4096

Step 2: Start server with config

$ litellm --config /path/to/config.yaml

Setting Embedding Models

See supported Embedding Providers & Models here

Use Sagemaker, Bedrock, Azure, OpenAI, XInference

Create Config.yaml

model_list:
- model_name: bedrock-cohere
litellm_params:
model: "bedrock/cohere.command-text-v14"
aws_region_name: "us-west-2"
- model_name: bedrock-cohere
litellm_params:
model: "bedrock/cohere.command-text-v14"
aws_region_name: "us-east-2"
- model_name: bedrock-cohere
litellm_params:
model: "bedrock/cohere.command-text-v14"
aws_region_name: "us-east-1"

Start Proxy

litellm --config config.yaml

Make Request

Sends Request to bedrock-cohere

curl --location 'http://0.0.0.0:8000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "bedrock-cohere",
"messages": [
{
"role": "user",
"content": "gm"
}
]
}'

Router Settings

Use this to configure things like routing strategy.

router_settings:
routing_strategy: "least-busy"

model_list: # will route requests to the least busy ollama model
- model_name: ollama-models
litellm_params:
model: "ollama/mistral"
api_base: "http://127.0.0.1:8001"
- model_name: ollama-models
litellm_params:
model: "ollama/codellama"
api_base: "http://127.0.0.1:8002"
- model_name: ollama-models
litellm_params:
model: "ollama/llama2"
api_base: "http://127.0.0.1:8003"

Max Parallel Requests

To rate limit a user based on the number of parallel requests, e.g.: if user's parallel requests > x, send a 429 error if user's parallel requests <= x, let them use the API freely.

set the max parallel request limit on the config.yaml (note: this expects the user to be passing in an api key).

general_settings:
max_parallel_requests: 100 # max parallel requests for a user = 100

All settings

{
"environment_variables": {},
"model_list": [
{
"model_name": "string",
"litellm_params": {},
"model_info": {
"id": "string",
"mode": "embedding",
"input_cost_per_token": 0,
"output_cost_per_token": 0,
"max_tokens": 2048,
"base_model": "gpt-4-1106-preview",
"additionalProp1": {}
}
}
],
"litellm_settings": {}, # ALL (https://github.com/BerriAI/litellm/blob/main/litellm/__init__.py)
"general_settings": {
"completion_model": "string",
"key_management_system": "google_kms", # either google_kms or azure_kms
"master_key": "string",
"database_url": "string",
"database_type": "dynamo_db",
"database_args": {
"billing_mode": "PROVISIONED_THROUGHPUT",
"read_capacity_units": 0,
"write_capacity_units": 0,
"ssl_verify": true,
"region_name": "string",
"user_table_name": "LiteLLM_UserTable",
"key_table_name": "LiteLLM_VerificationToken",
"config_table_name": "LiteLLM_Config",
"spend_table_name": "LiteLLM_SpendLogs"
},
"otel": true,
"custom_auth": "string",
"max_parallel_requests": 0,
"infer_model_from_keys": true,
"background_health_checks": true,
"health_check_interval": 300,
"alerting": [
"string"
],
"alerting_threshold": 0
}
}